> For the complete documentation index, see [llms.txt](https://xzhu0027.gitbook.io/blog/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://xzhu0027.gitbook.io/blog/security-privacy/untitled.md).

# Index

* [**RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response**](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/42852.pdf) - Erlingsson et al., CCS '14
  * Randomized Response + Bloom Filter
* [**Deep Learning with Differential Privacy**](https://arxiv.org/pdf/1607.00133.pdf) - Abadi et al., CCS '16 \[[Summary](https://xzhu0027.gitbook.io/blog/machine-learning/dl-fl-with-differential-privacy)]
  * Discussed how to train deep neural networks with non-convex objectives, under a modest privacy budget
* [**Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning**](https://arxiv.org/abs/1702.07464)\* - Hitaj et al., CCS '17
  * Proposed and implement an active inference attack on deep neural networks in a collaborative setting(which stresses the importance of using secure aggregation and differential privacy.)
* [**Opaque: An Oblivious and Encrypted Distributed Analytics Platform**](https://people.eecs.berkeley.edu/~wzheng/opaque.pdf) - Zheng et al., NSDI '17
* [**Prio: Private, Robust, and Scalable Computation of Aggregate Statistics**](https://www.usenix.org/system/files/conference/nsdi17/nsdi17-corrigan-gibbs.pdf) - Corrigan-Gibbs et al., NSDI '17
* [**Honeycrisp: Large-Scale Differentially Private Aggregation Without a Trusted Core**](https://www.cis.upenn.edu/~ahae/papers/honeycrisp-sosp2019.pdf) - Roth et al., SOSP '19
* [**Deep Leakage from Gradients**](https://arxiv.org/abs/1906.08935) **-** Zhu et al., NIPS '19 \[[Zhihu](https://www.zhihu.com/question/345365328/answer/930250128)]
* [**Shredder: Learning Noise Distributions to Protect Inference Privacy**](https://dl.acm.org/doi/pdf/10.1145/3373376.3378522) - Mireshghallah et al., ASPLOS ' 20
* [**Fawkes: Protecting Privacy against Unauthorized Deep Learning Models**](https://people.cs.uchicago.edu/~ravenben/publications/pdf/fawkes-usenix20.pdf) - Shan et al., Security '20
* [**Orchard: Differentially Private Analytics at Scale**](https://www.usenix.org/conference/osdi20/presentation/roth) - Roth- et al., OSDI '20


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